DocumentCode :
417500
Title :
Orthogonal decompositions of multivariate statistical dependence measures
Author :
Goodman, Ilan N. ; Johnson, Don H.
Author_Institution :
ECE Dept., Rice Univ., Houston, TX, USA
Volume :
2
fYear :
2004
fDate :
17-21 May 2004
Abstract :
We describe two multivariate statistical dependence measures which can be orthogonally decomposed to separate the effects of pairwise, triplewise, and higher order interactions between the random variables. These decompositions provide a convenient method of analyzing statistical dependencies between large groups of random variables, within which smaller "sub-groups" may exhibit dependencies separately from the rest of the variables. The first dependence measure is a generalization of Pearson\´s φ2, and we decompose it using an orthonormal series expansion of joint probability density functions. The second measure is based on the Kullback-Leibler distance, and we decompose it using information geometry. Applications of these techniques include analysis of neural population recordings and multimodal sensor fusion. We discuss in detail the simple example of three jointly defined binary random variables.
Keywords :
probability; random processes; series (mathematics); signal processing; statistical analysis; Kullback-Leibler distance; binary random variables; information geometry; joint probability density functions; multimodal sensor fusion; multivariate statistical dependence measures; neural population recordings; orthogonal decomposition; orthonormal series expansion; Density measurement; Encoding; Fuses; Information geometry; Multimodal sensors; Neurons; Pairwise error probability; Probability density function; Probability distribution; Random variables;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-8484-9
Type :
conf
DOI :
10.1109/ICASSP.2004.1326433
Filename :
1326433
Link To Document :
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